Release 3.2.0 by martin-springer · Pull Request #487 · NatLabRockies/rdtools

martin-springer · 2026-03-05T16:12:23Z

Summary

This PR collects the changes for the v3.2.0 release, including new multi-year-on-year degradation analysis features, plotting improvements, bug fixes, and expanded test coverage.

Enhancements

degradation_year_on_year — label parameter (degradation_year_on_year should allow center-labeled YoY_values #459): New label= parameter ('right', 'center', or 'left') controls which edge of the YoY slope is used for labeling calc_info['YoY_values']. Default is 'right' (backward-compatible).
degradation_year_on_year — multi_yoy parameter (Multi-YoY analysis based on Hugo Quest et al 2023 #394): New multi_yoy= parameter (default False) enables multi-year-on-year degradation calculations similar to Hugo Quest et al 2023, computing slopes over N × 365 day separations.
degradation_year_on_year — YoY_times output (degradation_year_on_year should allow center-labeled YoY_values #459): calc_info['YoY_times'] now returns a DataFrame with dt_right, dt_center, and dt_left columns for each slope.
degradation_timeseries_plot improvements (degradation_timeseries_plot should have different label= options #455): Rolling median is now centered (center=True) and min_periods reduced from rolling_days//2 to rolling_days//4.
degradation_timeseries_plot multi-YoY support (Multi-YoY analysis based on Hugo Quest et al 2023 #394): Handles multi_yoy=True data by resampling overlapping YoY values to their daily mean (with a warning).
degradation_summary_plots parity coloring (Multi-YoY analysis based on Hugo Quest et al 2023 #394): detailed=True mode now properly colors points by odd/even usage count, not just 0/1/2.
sensor_analysis / clearsky_analysis: Now explicitly default yoy_kwargs={"label": "right"}.
New example notebook: docs/Multi-year_on_year_example.ipynb demonstrates label='center' and multi_yoy=True (Multi-YoY analysis based on Hugo Quest et al 2023 #394).

Bug Fixes

Fixed usage_of_points calculation in degradation_year_on_year to properly handle multi_yoy=True mode with overlapping slopes (Multi-YoY analysis based on Hugo Quest et al 2023 #394).

Maintenance

Added _avg_timestamp_old_Pandas helper for pandas <2.0 compatibility when calculating center labels.
Fixed nbval workflow command syntax (--sanitize-with → --nbval-sanitize-with).
Improved pandas 3.0 compatibility with datetime resolution handling.
Updated docs/notebook_requirements.txt to require numexpr>=2.10.2 and tabulate>=0.9.0.
Removed trailing semicolons in example notebooks.
Added .coverage.* pattern to .gitignore.

Testing

Added error-handling tests for analysis_chains: filter_params validation, clearsky_rescale_index_mismatch, poa_filter_without_poa, tcell_filter_without_temperature, hour_angle_filter_without_location, clearsky_filter_without_poa, degradation_timeseries_plot_invalid_case.
Added error-handling tests for degradation: classical_decomposition missing/irregular data, year_on_year circular block validation, no valid pairs, _mk_test edge cases.
Added tests for multi_yoy=True in degradation_year_on_year.
Added tests for degradation_timeseries_plot covering label='center', label='left', multi-YoY duplicate index, and KeyError path.
Set matplotlib backend to Agg in conftest.py to avoid tkinter issues.

Checklist

Code changes are covered by tests
Code changes have been evaluated for compatibility/integration with TrendAnalysis
New functions added to __init__.py
API.rst is up to date, along with other sphinx docs pages
Example notebooks are rerun and differences in results scrutinized
Updated changelog

* add keyword 'label' to degradation_timeseries_plot, enabling 'left' and 'center' labeling options. * Update changelog, add pytests, update sphinx documentation * fix flake8 grumbles * update pytests to include axes limits * fix flake8 grumbles * add 'label' input option to `degradation_year_on_year`. Fixes #459 * add pytests and update changelog. * flake8 grumbles * Minor updates to setup.py (constrain scipy<1.16) and refactor degradation_test * Custom fix for Pandas < 2.0.0 which can't average two columns of timestamps. * flake8 grumbles * keep TZ-aware timestamps. Update pytests to specifically test _avg_timestamp_old_Pandas * flake8 grumbles * try to UTC localize the pytest... * Add .asfreq() to get pytests to agree * switch to calendar.timegm to hopefully remove TZ issues.. * regardless of uncertainty_method, return calc_info{'YoY_values') * update _right dt labels to correct _left labels in degradation_year_on_year * update _avg_timestamp_old_Pandas to allow for numeric index instead of timestamp * add left label option to degradation_year_on_year * update degradation_year_on_year, index set to either left, center or right. Consistent with #394 - multi_yoy * update return for default = none uncertainty option * degradation_year_on_year - go back to single return when uncertainty_value = None to avoid breaking pytests. * add multi-year aggregation of slopes in degradation_year_on_year * add multi_yoy kwarg in degradation_year_on_year to toggle the multi-YoY function. * update plotting for detailed=True, allow usage_of_points > 2 * flake8 grumbles * update plotting detailed=True for (even) and (odd) number of points coloring * To allow multi_yoy=True in plotting.degradation_timeseries_plot, resample.mean() the YoY_values. * flake8 grumbles * Add warning to degradation_timeseries_plot when multi_YoY=True * update to warning message in plotting.degradation_timeseries_plot * fix flake8 grumbles * nbval fixes from qnguyen345-bare_except_error * Add pandas 3.0 futurewarning handling * Try again to solve pandas3.0 futurewarning * attempt 3 to fix nbval * Add infer_objects to remove futurewarning * minor inline comment update * update plotting tests to be relative value, update ordering of module import in plotting.py, per Copilot review. * update inline comments and whatsnew docs * Clean up inline comments per Copilot review * added multi-YoY pytest - still need to catch warnings * Add a warnings.catch_warnings to the plotting pytest * flake8 grumbles * add multi-YoY=True pytest * updated changelog * update changelog * implement copilot suggestions * linting * use s instead of ns for pandas 3 compatibility * update pandas version comparison * set matplotlib non-gui backend for tests * set dtype resolution based on pandas version * linting import order * boost degradation.py test coverage * update changelog * exclude coverage reports with suffixes * Update rdtools/degradation.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * remove label=None handling, rely on default 'right' behavior * refactor dt_center tz handling for old pandas * simplify _avg_timestamp_old_Pandas * degradation_timeseries_plot: change rolling median min_periods to rolling_days / 4. * remove degradation_timeseries_plot(label=) and just default to center=True * update sensor_analysis() and clearsky_analysis() docstrings to discuss passing `label=right` kwargs * flake8 updates * Initial commit - multi-YoY notebook * pretty-print notebook dataframes with tabulate. * update notebook requirements to silence pandas warnings * add multi-yoy nb to tutorials * fix nblink path * Change the yoy_values index to be named 'dt'. Add new illustrations at the end of the multi-YoY notebook. --------- Co-authored-by: Michael Deceglie <mdeceglie@users.noreply.github.com> Co-authored-by: martin-springer <martinspringer.ms@gmail.com> Co-authored-by: Martin Springer <97482055+martin-springer@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* add keyword 'label' to degradation_timeseries_plot, enabling 'left' and 'center' labeling options. * Update changelog, add pytests, update sphinx documentation * fix flake8 grumbles * update pytests to include axes limits * fix flake8 grumbles * add 'label' input option to `degradation_year_on_year`. Fixes #459 * add pytests and update changelog. * flake8 grumbles * Minor updates to setup.py (constrain scipy<1.16) and refactor degradation_test * Custom fix for Pandas < 2.0.0 which can't average two columns of timestamps. * flake8 grumbles * keep TZ-aware timestamps. Update pytests to specifically test _avg_timestamp_old_Pandas * flake8 grumbles * try to UTC localize the pytest... * Add .asfreq() to get pytests to agree * switch to calendar.timegm to hopefully remove TZ issues.. * regardless of uncertainty_method, return calc_info{'YoY_values') * update _right dt labels to correct _left labels in degradation_year_on_year * update _avg_timestamp_old_Pandas to allow for numeric index instead of timestamp * add left label option to degradation_year_on_year * update degradation_year_on_year, index set to either left, center or right. Consistent with #394 - multi_yoy * update return for default = none uncertainty option * degradation_year_on_year - go back to single return when uncertainty_value = None to avoid breaking pytests. * add multi-year aggregation of slopes in degradation_year_on_year * add multi_yoy kwarg in degradation_year_on_year to toggle the multi-YoY function. * update plotting for detailed=True, allow usage_of_points > 2 * flake8 grumbles * update plotting detailed=True for (even) and (odd) number of points coloring * To allow multi_yoy=True in plotting.degradation_timeseries_plot, resample.mean() the YoY_values. * flake8 grumbles * Add warning to degradation_timeseries_plot when multi_YoY=True * update to warning message in plotting.degradation_timeseries_plot * fix flake8 grumbles * nbval fixes from qnguyen345-bare_except_error * Add pandas 3.0 futurewarning handling * Try again to solve pandas3.0 futurewarning * attempt 3 to fix nbval * Add infer_objects to remove futurewarning * minor inline comment update * update plotting tests to be relative value, update ordering of module import in plotting.py, per Copilot review. * update inline comments and whatsnew docs * Clean up inline comments per Copilot review * added multi-YoY pytest - still need to catch warnings * Add a warnings.catch_warnings to the plotting pytest * flake8 grumbles * add multi-YoY=True pytest * updated changelog * update changelog * implement copilot suggestions * linting * use s instead of ns for pandas 3 compatibility * update pandas version comparison * set matplotlib non-gui backend for tests * set dtype resolution based on pandas version * linting import order * boost degradation.py test coverage * add tests for error handling in analysis_chain * update changelog * update changelog * exclude coverage reports with suffixes * Update rdtools/degradation.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * remove label=None handling, rely on default 'right' behavior * refactor dt_center tz handling for old pandas * simplify _avg_timestamp_old_Pandas * degradation_timeseries_plot: change rolling median min_periods to rolling_days / 4. * remove degradation_timeseries_plot(label=) and just default to center=True * update sensor_analysis() and clearsky_analysis() docstrings to discuss passing `label=right` kwargs * flake8 updates * Initial commit - multi-YoY notebook * pretty-print notebook dataframes with tabulate. * update notebook requirements to silence pandas warnings * add multi-yoy nb to tutorials * fix nblink path * Change the yoy_values index to be named 'dt'. Add new illustrations at the end of the multi-YoY notebook. * clean up pending changelog --------- Co-authored-by: cdeline <chris.deline@nrel.gov> Co-authored-by: Michael Deceglie <mdeceglie@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Copilot

Pull request overview

This PR aggregates changes for the v3.2.0 release, centered on expanding the year-on-year (YoY) degradation workflow (labeling + multi-YoY), improving degradation plotting behavior, and adding broader error-handling test coverage and documentation updates.

Changes:

Extend degradation_year_on_year with label (right|center|left), multi_yoy, and new calc_info['YoY_times'] output, plus updated usage_of_points handling for multi-YoY.
Update degradation_timeseries_plot rolling behavior (centered rolling median, lower min_periods) and add fallback handling for multi-YoY/duplicate-index data.
Add/expand tests, docs, notebooks, CI notebook validation, and release changelog for v3.2.0.

Reviewed changes

Copilot reviewed 15 out of 18 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
`rdtools/degradation.py`	Adds `label`/`multi_yoy` support, `YoY_times`, and updates `usage_of_points` computation.
`rdtools/plotting.py`	Adjusts degradation timeseries rolling behavior and adds multi-YoY plotting fallback/warning.
`rdtools/analysis_chains.py`	Changes default `yoy_kwargs` to explicitly default `label="right"` in analysis chains.
`rdtools/test/plotting_test.py`	Adds coverage for label alignment behavior and multi-YoY warning path in plotting.
`rdtools/test/degradation_test.py`	Adds tests for center labeling, multi-YoY, pandas-compat helper, and more error-path coverage.
`rdtools/test/conftest.py`	Sets matplotlib backend to `Agg` before importing `rdtools` to stabilize headless CI.
`rdtools/test/analysis_chains_test.py`	Adds tests for setter validation and additional error paths.
`docs/sphinx/source/examples/Multi-year_on_year_example.nblink`	Adds new example notebook link for multi-YoY/label usage.
`docs/sphinx/source/examples.rst`	Registers the new example in Sphinx examples.
`docs/sphinx/source/changelog/v3.2.0.rst`	Introduces v3.2.0 changelog entry.
`docs/sphinx/source/changelog.rst`	Includes pending + v3.2.0 changelog files.
`docs/notebook_requirements.txt`	Updates notebook dependency pins (incl. `numexpr`, `tabulate`).
`docs/TrendAnalysis_example*.ipynb`	Removes trailing semicolons/cleans notebook cells.
`.github/workflows/nbval.yaml`	Fixes nbval sanitize CLI usage.
`.gitignore`	Adds `.coverage.*` and fixes comment typo.

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cdeline · 2026-03-05T21:31:55Z

@martin-springer : I'm getting setup.py deprecation messages when I install. Would this be the time to switch to a pyproject.toml?

Copilot · 2026-03-05T21:44:34Z

@cdeline I've opened a new pull request, #489, to work on those changes. Once the pull request is ready, I'll request review from you.

Copilot · 2026-03-05T21:46:20Z

@cdeline I've opened a new pull request, #490, to work on those changes. Once the pull request is ready, I'll request review from you.

martin-springer · 2026-03-05T23:51:08Z

@martin-springer : I'm getting setup.py deprecation messages when I install. Would this be the time to switch to a pyproject.toml?

@cdeline - yes, we should do that. I'll open a PR and make the changes.

codecov-commenter · 2026-03-06T00:26:39Z

Codecov Report

❌ Patch coverage is 94.44444% with 4 lines in your changes missing coverage. Please review.
✅ Project coverage is 96.87%. Comparing base (d0bb8fa) to head (6238de5).

Files with missing lines	Patch %	Lines
rdtools/degradation.py	95.45%	2 Missing ⚠️
rdtools/plotting.py	92.30%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #487      +/-   ##
==========================================
+ Coverage   96.27%   96.87%   +0.60%     
==========================================
  Files          12       12              
  Lines        2280     2339      +59     
==========================================
+ Hits         2195     2266      +71     
+ Misses         85       73      -12

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…lti-YoY detection (#490) * Initial plan * Replace broad except ValueError with explicit duplicate-index check in degradation_timeseries_plot Co-authored-by: cdeline <23244088+cdeline@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: cdeline <23244088+cdeline@users.noreply.github.com>

mdeceglie · 2026-03-20T20:23:59Z

With calc_info['YoY_values'['dt_right', 'dt_center', 'dt_left'] why do we also need to introduce the label parameter, this seems redundant?

mdeceglie · 2026-03-20T20:29:16Z

+    if results_values.index.has_duplicates:
+        # this occurs with degradation_year_on_year(multi_yoy=True). resample to daily mean
+        warnings.warn(
+            "Input `yoy_info['YoY_values']` appears to have multiple annual "
+            "slopes per day, which is the case if "
+            "degradation_year_on_year(multi_yoy=True). "
+            "Proceeding to plot with a daily mean which will average out the "
+            "time-series trend. Recommend re-running with "
+            "degradation_year_on_year(multi_yoy=False)."
+        )
+        roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
+                                                             min_periods=rolling_days//4,
+                                                             center=True)


I'm unclear on why this is needed. At the end of the day we're plotting a median of all the relevant slopes within rolling_days why not just throw the multi-yoy slopes in with the rest of the sample instead of taking an intermediate mean?

This may be a limitation of the pd.rolling function which I believe requires a unique timeindex to operate on. I can investigate options here.

I'm opening a side branch 'multi_YoY_no_label' to refactor around removing the label= kwarg in degradation_year_on_year and re-write degradation_timeseries_plot() to plot the median of all slopes that have either their starting or ending point within the rolling_days window.

cdeline · 2026-03-20T20:35:36Z

With calc_info['YoY_values'['dt_right', 'dt_center', 'dt_left'] why do we also need to introduce the label parameter, this seems redundant?

The main thing that label= does is to change the timestamp in the calc_info['YoY_values'] timeindex to either match right, center or left. That is the timeindex that gets passed into the plotting functions, so I suppose it mainly affects the degradation_timeseries_plot.

mdeceglie · 2026-03-20T20:47:57Z

Which label is the best one to use, especially when multi-yoy is in play? Center?

cdeline · 2026-03-23T17:53:37Z

Which label is the best one to use, especially when multi-yoy is in play? Center?

Yes I would definitely recommend center-labeling of the 'YoY_values' timeseries, particularly with multi-yoy analysis. If we get rid of the label= kwarg option in degradation_year_on_year() I would recommend changing from the default of right-labeled to center-labeled.

mdeceglie · 2026-03-24T13:54:56Z

Well we can't change the default behavior of calc_info['YoY_values'] until version 4. But I think we can achieve all the functionality here but just adding the calc_info['YoY_times']. Since degradation_timeseries_plot gets calc_info as it's argument, it can make the right decision about how to plot.

I suggest that degradation_timeseries_plot get a key word argument that tells it which of the columns in calc_info['YoY_times'] to use. Since the current default behavior is confusing/not ideal, let's consider whether we do a work around wherein if the new kwarg is not passed, the old behavior prevails but with a warning that the new best practice is e.g. slope_centering=True, and use the new kwarg with the preferred value in the examples.

My concern here is twofold:

The current changes to degradation_year_on_year are redundant
We are trying to control the behavior of degradation_timeseries_plot by proxy through degradation_year_on_year instead of in the plotting function itself

* updated degradation_timeseries_plot as version2 for now * fix flake8 grumbles * avoid pandas error "ValueError: Other Series must have a name" * Redo degradation_timeseries_plot - include in median if dt_center is within rolling window. * remove label= functionality. Update Multi-year_on_year_example.ipynb * Re-run Multi-year_on_year_example.ipynb, check for text changes. * update analysis_chains to remove `label=`. Update whatsnew * simplify multi-YoY timeseries plotting by filtering to slopes <= 2 years, removing warning. * update whatsnew * code cleanup * Revision of degradation_timeseries_plot * Bug fixes * Update notebooks to include `center=True` * Update nbval to include new notebook * add new kwarg to TrendAnalysis.plot_degradation_timeseries * Update plotting.py timeseries plot docstring. Also set pd.rolling(min_periods=rolling_days//4) * fix copilot documentation review recommendations; add plotting pytests * add min_periods_divisor argument * update pvdaq_system_4 url (#513) * update pvdaq_system_4 url * changelog * revert unnecessary changes in notebooks * update changelog * re-run notebooks --------- Co-authored-by: Michael Deceglie <Michael.Deceglie@nrel.gov> Co-authored-by: martin-springer <martin.springer@nrel.gov> Co-authored-by: Martin Springer <97482055+martin-springer@users.noreply.github.com>

mdeceglie

This is looking pretty close! In addition to my contextual comments, a few comments on the example notebooks:

Change the links to the example data in the notebooks' markdown sections
Let's add some more explanation to the TrendAnalysis example notebook plot_degradation_timeseries part. Something like: "In this case the fluctuations in apparent degradation rate are probably due to the soiling component of the signal. This example illustrates the pitfalls of attempting to look at how degradation rate evolves in time in the presence of a large uncorrected soiling signal. Nevertheless it illustrates the plotting functionality that can be useful in applications not affected by soiling or other reversible changes."

mdeceglie · 2026-06-26T17:26:04Z

+Enhancements
+------------
+* :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``multi_yoy``
+  (default False) to trigger multiple YoY degradation calculations similar to Hugo Quest et


Let's include a citation to the Quest paper

mdeceglie · 2026-06-26T17:27:19Z

@@ -0,0 +1,78 @@
+*************************
+v3.2.0 (X, X, 2026)


We need to add an explanation that the index of YoY_values has changed from a timestamp to integer and that the original behavior can be reconstructed with the help of calc_info['YoY_times']. Strictly speaking, I don't think this is backward compatible, but for this case I think it may be ok to keep it in a minor version update.

mdeceglie · 2026-06-26T17:29:38Z

@@ -0,0 +1,78 @@
+*************************
+v3.2.0 (X, X, 2026)
+*************************


Another even more in the weeds changes is that we have replaced NaNs in calc_info['usage_of_points'] with zeros. I don't know if this ever happens in practice but probably worth it to call out the edge case change.

mdeceglie · 2026-06-26T17:30:37Z

@@ -218,7 +224,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
        degradation rate estimate
    calc_info : dict


should add explanation of calc_info['YoY_times']

cdeline and others added 3 commits March 5, 2026 11:00

rename pending to v3.2.0

1476f02

martin-springer requested a review from Copilot March 5, 2026 16:13

Copilot started reviewing on behalf of martin-springer March 5, 2026 16:17 View session

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martin-springer requested review from cdeline and mdeceglie March 5, 2026 16:38

Copilot AI mentioned this pull request Mar 5, 2026

Fix mutable default arguments in sensor_analysis and clearsky_analysis #489

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Copilot AI mentioned this pull request Mar 5, 2026

Fix broad except ValueError in degradation_timeseries_plot for multi-YoY detection #490

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cdeline added 2 commits March 5, 2026 14:47

Update docstrings and address simple comments from copilot

b25219d

flake8 grumbles

1d2d060

Copilot AI and others added 2 commits March 6, 2026 11:09

Merge branch 'master' into development

3c594db

mdeceglie reviewed Mar 20, 2026

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cdeline and others added 2 commits June 24, 2026 17:11

Merge remote-tracking branch 'origin/master' into development

8b22e2e

martin-springer requested a review from mdeceglie June 25, 2026 12:43

re-run TrendAnalysis nb

6238de5

mdeceglie reviewed Jun 26, 2026

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		@@ -0,0 +1,78 @@
		*************************
		v3.2.0 (X, X, 2026)

		@@ -218,7 +224,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
		degradation rate estimate
		calc_info : dict

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